Weighing the Value of Apache Hadoop vs. Cloudera

As the Big Data landscape has changed, comparing Apache Hadoop vs. Cloudera and their commercial platform is a worthwhile exercise. Do enterprise teams still need Cloudera for their Big Data stack management or can they save by independently managing their Apache Hadoop implementation?

In this blog, we'll take a close look at the value of the Cloudera platform's software bundle, proprietary tools, and cloud-hosting services. We’ll also explore Cloudera alternativesfor organizations that would prefer to not migrate to the cloud and want the freedom to decide where and how to manage their data infrastructure. 

Note: In this blog, references to the Cloudera platform are meant to encompass both the Cloudera Data Platform (CDP) and the legacy product, Cloudera Distribution of Hadoop (CDH).

Apache Hadoop vs. Cloudera: What's the Difference?

Apache Hadoop is a free, open source data-processing technology that uses a network of computers to solve large data computation via the MapReduce programming model. Cloudera offers a commercial, Hadoop-based platform that is available via paid subscription.

The Cloudera platform is based on Apache Hadoop and various other software packages that, by and large, are part of the broader Apache Hadoop ecosystem. Therefore, many of the features and functions of Cloudera's platform are available for free via the collection of those foundational open source software packages. 

When customers pay for a Cloudera subscription, they are essentially paying for:

• A curated bundle of the open source software packages and specific versions that have been validated and proven to work together.
• A couple of proprietary (not open source) applications that provide conveniences intended to help adopters manage an implementation of these disparate open source software packages.
• A hosted managed services provider that unites it all in a controlled environment with the promise of stability, availability, and carefree maintenance.

While valuable for some enterprise use cases, these benefits come at a price — particularly the last one, as cloud migrations can be expensive. Because the Big Data landscape is continuously evolving with new solutions coming on the market all the time, it is a good practice to regularly evaluate the return on investment of those features against the cost of managing an equivalent open source stack. 

In the next few sections, we'll dig deeper into the three bullets mentioned above and compare them to the free equivalents in Apache Hadoop.

1. Cloudera's Curated Bundle of OSS

When the Hadoop Ecosystem was an emerging technology, it was beneficial to have a leader in the space like Cloudera piecing together and testing a set of immature open source technologies that were under active development. Cloudera made it so individual companies did not have to dedicate development resources to keep pace with many independently evolving software releases and ensure there were no breaking changes at all the integration points. This can be particularly painful for early adopters, as there are rarely standards or best practices in place to allow product features to evolve independently. Without standards, the products are more tightly coupled and implementations must be more closely managed. 

The situation today, however, is very different. For example, many products now rely on JSON or YAML as the agreed-upon data exchange formats, but those were not in place 20 years ago. Data formats like Parquet and Avro take this a step further. Likewise, there are best practices around RESTful API versioning that many products now implement — and the list goes on. So what would have been very burdensome and resource-draining when Hadoop first emerged is considerably more feasible these days because standards and best practices have caught up. 

This is not to say a controlled and validated environment isn’t a good thing. It just might not deliver as much ROI for organizations as it once did. Furthermore, one must reevaluate being locked into a bundle vs. having flexibility now that more innovative and impactful technologies are available. Specifically, there are a couple of foundational areas where Apache Hadoop has made considerable advancements compared to what you get with the Cloudera implementation of Hadoop, and that's what we will cover next. 

Execution Services: Oozie vs. Airflow

At a time when more modern organizations are moving toward Apache Airflow for workflow, Cloudera is still shipping with, and relying on, Apache Oozie. Apache Oozie workflows are tied to the Hadoop ecosystem and require unwieldy XML-based definitions. In contrast, Apache Airflow is a more modern, flexible, and scalable workflow and data pipeline management tool that integrates well with cloud services and various systems beyond Hadoop. It has a friendly user interface, a strong community, and advanced error handling. 

Security Services: Navigator & Sentry vs. Atlas & Ranger 

Modern Apache Hadoop implementations use a combination of Apache Atlas and Apache Ranger. Both of these products achieve significant improvements over the legacy Navigator and Sentry. Atlas will be covered again later when highlighting data governance. Apache Ranger has a more user-friendly web-based interface that makes it easier to create and manage security policies. Unlike Sentry, Ranger includes built-in robust auditing capabilities for tracking events and activities across the platform, even outside of Hadoop proper.

To be fair, Cloudera is migrating to these improved options as well, but they are not there yet — leaving CDP implementers saddled with the complexity of a combined solution but unable to benefit from the full set of new features.

Ready to Open Source Your Big Data Management?

With the Hadoop Service Bundle, enterprises can keep their data on-prem, avoid compliance headaches and cloud-hosting fees, and save up to 60% in annual costs. If you are looking for more freedom and flexibility when it comes to your Big Data infrastructure, switch from Cloudera to a fully open source Hadoop implementation supported by OpenLogic.

Hadoop Service Bundle Details

2. Cloudera's Proprietary Tools for Cluster Management, Cluster Administration, and Data Governance

Cloudera ships two proprietary applications, Cloudera Manager and Cloudera Navigator, to provide implementors with a toolkit for managing and administering their Hadoop Cluster. These applications are essential in offering a cohesive, professional, and useful Hadoop-based Big Data platform. 

However, there are open source alternatives that meet or beat the features available in these proprietary tools. In fact, the most predominant open source versions of these tools were originally developed in the open and handed over to the Apache Foundation by Hortonworks — a company that was purchased by Cloudera in 2019. 

Cloudera Manager vs. Ambari

Cloudera Manager is an administrative application for the Cloudera Data Platform (CDP). It has a web-based user interface and a programmatic API, and is used to provision, configure, manage, and monitor CDP-based Hadoop clusters and associated services.

Apache Hadoop implementors use Apache Ambari (a project with Hortonworks origins) to accomplish what is offered through Cloudera Manager on CDP Hadoop implementations. Apache Ambari has a web-based user interface and a programmatic REST API that allows organizations to provision, manage, and administer Hadoop clusters and associated services.

To take a deeper dive and learn more about the nuanced differences between these tools, see my previous blog: Apache Ambari vs Cloudera Manager. 

Cloudera Navigator vs. Apache Atlas

Cloudera Navigator handles data governance. It offers a wide range of features for auditing and compliance, from organization policy creation and tracking to regulatory requirements like GDPR and HIPPA. It also includes data lineage tracking to look back upon data transformation and evolution, as well as metadata management for tagging and categorizing data to assist in searching and filtering.

Apache Hadoop implementors use Apache Atlas (also originally developed by Hortonworks) to implement data governance and metadata management. Cloudera Navigator is only applicable to CDP, whereas Apache Atlas works across a broad range of Hadoop distributions and data ecosystems. It is extensible and integrates with other packages, like Apache Hive and Apache HBase.

Apache Atlas logs creation, modification, access, and lineage information about each data asset. It tracks who has accessed or modified data to provide an audit trail for compliance and monitoring purposes. Policies can be defined in Atlas to manage role-based access control (RBAC), attribute-based access control (ABAC), and data masking. To enforce these policies, Atlas integrates with Apache Ranger (another open source package in the Hadoop ecosystem).

3. Cloudera's Cloud-Hosting Environment and Managed Services

Measuring the value of where the infrastructure resides will likely be more of a policy question for most organizations. Most organizations have a preference or a requirement that dictates whether they host services in public, private, on-premises, or hybrid clouds. So the real assessment here lies more in the value aligned with the managed services offered by Cloudera. For organizations that are not required to manage and own their own infrastructure, and don't mind paying for these managed services, this may tip the scales in Cloudera's favor. 

However, organizations that don't want to be forced to the cloud should consider whether they have the talent, motivation, and capacity to own and maintain an Apache Hadoop implementation. The maturity of the Hadoop ecosystem and the availability of standardized cloud resources make this a viable alternative to Cloudera — but only if you have the internal resources or a partner like OpenLogic with deep Apache Hadoop expertise.

Other Considerations 

We outlined some key differences in cluster execution services, cluster security, cluster administration, and data governance between Apache Hadoop and CDP. However, there are a number of other features and functions that are nearly identical for both of these platforms that will require installation, configuration, care, and feeding. These include products like Zookeeper for cluster coordination, and a number of data services that can be applied to meet various needs of an organization. These include, but are not limited to, HDFS, MapReduce, Yarn, Apache Spark, Apache Kafka, HBase, Hive, and Hue.

Final Thoughts

There was a time when it was easier to associate a clear value for the dollar spend on Cloudera. They were pioneers in Big Data and offered the first commercial bundle of Hadoop. They were the Hadoop provider for many of the Fortune 500 firms. The Cloudera Platform could speed time to market, providing a clear path to a stable Big Data environment that allowed implementers to focus on creating domain-specific applications that leveraged their data — rather than juggling between managing a data platform and making use of their data.

However, nearly two decades have passed since the first incarnation of Hadoop. Cloudera has been involved for over 15 years, and a lot has changed. Hadoop has matured dramatically, and the supporting ecosystem has grown. New open source solutions are being developed all the time, as well as new commercial offerings around Big Data services and support. While there is still an appetite for hands-off, fully managed Big Data platforms like the one that Cloudera offers, the price has driven demand for lower-cost alternatives. For some organizations, using Apache Hadoop and avoiding a costly cloud migration is priceless.  

Additional Resources

• Webinar - Is It Time to Open Source Your Big Data Management?
• Blog - Introducing the Hadoop Service Bundle From OpenLogic
• Solution - Hadoop Service Bundle 
• Solution - Apache Hadoop Support
• Datasheet - Hadoop Service Bundle
• Blog - Apache Ambari vs. Cloudera Manager
• Blog - A Second Wind for Apache Ambari
• Blog - Are You Locked In to Commercial Software?
• Blog - Spark vs. Hadoop: Key Differences and Use Cases
• Blog - When to Choose Kafka vs. Hadoop